To have lesser memory requirements, model quantization is now attracting to the researchers to design tinyML models. The accuracy of a model can be significantly degraded if it is uniformly quantized to low bit precision. It is possible to address this with mixed-precision quantization in which each layer is quantized with different bit precision. The main idea behind mixed precision quantization is to keep sensitive layers at higher precision and insensitive layers at lower precision. However, the search space for this bit setting grows exponentially as the number of layers increase, which is challenging. Various methods have been offered to deal with this enormous search area. Reinforcement learning (RL) and Neural Architecture Search (NAS) have recently been presented as efficient approaches for searching the search space. As a result, these methods [1]}, [2]}, [3]} often require a huge amount of computational resources and their performance is highly dependent on hyperparameters and even initialization. An algorithm known as Integer Linear Programming (ILP) is employed in [4]}, [5]}. ILP is very light weight and gives result within second. We adopted ILP and formulated our problem following the methodology described in [4]}, simplifying some constraints to get the mixed precision settings for our TinyM\(^2\) Net. ILP equations were solved using a python module called Pulp.
